Research Article

The iOSC3 System: Using Ontologies and SWRL Rules for Intelligent Supervision and Care of Patients with Acute Cardiac Disorders

Table 1

Comparison of iOSC3 with related medical expert systems. Each system is classified according to the categories proposed in [24]. The last column summarizes the main techniques or technologies used for knowledge representation and processing.

SystemCategoryApplicationTechniques/technologies

iOSC3Ontology-based, rule-basedDecision support in cardiac ICUsOWL ontology, SWRL rules, Pellet reasoner
Chen et al. (2012) [23]Ontology-based,
rule-based
Antidiabetic drugs selectionOWL ontology, SWRL rules, JESS engine
ODDIN (2010) [19]Ontology-based, rule-based, probabilisticDifferential diagnosis in medicineOWL ontology, Jena rules
Nocedal et al. (2010) [21]Rule-basedBreast cancer treatmentOWL ontology, inference rules
Kumar et al. (2009) [17]Case-based, rule-basedClinical decision support in ICUsRules in XML format
Blum et al. (2009) [22]Intelligent agents, rule-basedImproving physiologic alarms in critical careInference engine implemented using a stored SQL procedure
Karabatak and Ince (2009) [1]Association rules, neural networkBreast cancer detectionAssociation rules for feature extraction and multilayer perceptron for intelligent classification
Si et al. (1998) [18]Fuzzy logic, neural networkElectroencephalogram monitoring in pediatric ICUsStatistical comparison of features, fuzzy logic for feature classification, and neural networks for EEG assessment